[v0][structured output] Support reasoning output (#12955)

Signed-off-by: Ce Gao <cegao@tensorchord.ai>

examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+An example shows how to generate structured outputs from reasoning models
+like DeepSeekR1. The thinking process will not be guided by the JSON
+schema provided by the user. Only the final output will be structured.
+
+To run this example, you need to start the vLLM server with the reasoning 
+parser:
+
+```bash
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
+     --enable-reasoning --reasoning-parser deepseek_r1
+```
+
+This example demonstrates how to generate chat completions from reasoning models
+using the OpenAI Python client library.
+"""
+
+from enum import Enum
+
+from openai import OpenAI
+from pydantic import BaseModel
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+
+# Guided decoding by JSON using Pydantic schema
+class CarType(str, Enum):
+    sedan = "sedan"
+    suv = "SUV"
+    truck = "Truck"
+    coupe = "Coupe"
+
+
+class CarDescription(BaseModel):
+    brand: str
+    model: str
+    car_type: CarType
+
+
+json_schema = CarDescription.model_json_schema()
+
+prompt = ("Generate a JSON with the brand, model and car_type of"
+          "the most iconic car from the 90's, think in 100 tokens")
+completion = client.chat.completions.create(
+    model=model,
+    messages=[{
+        "role": "user",
+        "content": prompt,
+    }],
+    extra_body={"guided_json": json_schema},
+)
+print("content", completion.choices[0].message.content)
+print("reasoning_content: ", completion.choices[0].message.reasoning_content)